Inferring structural variant cancer cell fraction

Marek Cmero (), Ke Yuan, Cheng Soon Ong, Jan Schröder, Niall M. Corcoran, Tony Papenfuss, Christopher M. Hovens, Florian Markowetz and Geoff Macintyre ()
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Marek Cmero: Royal Melbourne Hospital and University of Melbourne
Ke Yuan: University of Glasgow, Sir Alwyn Williams Building
Cheng Soon Ong: University of Melbourne
Jan Schröder: Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research
Niall M. Corcoran: Royal Melbourne Hospital and University of Melbourne
Tony Papenfuss: Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research
Christopher M. Hovens: Royal Melbourne Hospital and University of Melbourne
Florian Markowetz: University of Cambridge
Geoff Macintyre: University of Melbourne

Nature Communications, 2020, vol. 11, issue 1, 1-15

Abstract: Abstract We present SVclone, a computational method for inferring the cancer cell fraction of structural variant (SV) breakpoints from whole-genome sequencing data. SVclone accurately determines the variant allele frequencies of both SV breakends, then simultaneously estimates the cancer cell fraction and SV copy number. We assess performance using in silico mixtures of real samples, at known proportions, created from two clonal metastases from the same patient. We find that SVclone’s performance is comparable to single-nucleotide variant-based methods, despite having an order of magnitude fewer data points. As part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types, we use SVclone to reveal a subset of liver, ovarian and pancreatic cancers with subclonally enriched copy-number neutral rearrangements that show decreased overall survival. SVclone enables improved characterisation of SV intra-tumour heterogeneity.

Date: 2020
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DOI: 10.1038/s41467-020-14351-8

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